Overview

Dataset statistics

Number of variables14
Number of observations41237
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 MiB
Average record size in memory112.0 B

Variable types

Numeric12
Categorical2

Warnings

address is highly correlated with name and 2 other fieldsHigh correlation
name is highly correlated with addressHigh correlation
location is highly correlated with addressHigh correlation
reviews_list is highly correlated with address and 1 other fieldsHigh correlation
city is highly correlated with reviews_listHigh correlation
address is highly correlated with name and 2 other fieldsHigh correlation
name is highly correlated with addressHigh correlation
rate is highly correlated with votesHigh correlation
votes is highly correlated with rateHigh correlation
location is highly correlated with addressHigh correlation
reviews_list is highly correlated with address and 1 other fieldsHigh correlation
city is highly correlated with reviews_listHigh correlation
address is highly correlated with nameHigh correlation
name is highly correlated with addressHigh correlation
rate is highly correlated with votesHigh correlation
votes is highly correlated with rateHigh correlation
reviews_list is highly correlated with cityHigh correlation
city is highly correlated with reviews_listHigh correlation
reviews_list is highly correlated with location and 5 other fieldsHigh correlation
votes is highly correlated with rateHigh correlation
location is highly correlated with reviews_list and 5 other fieldsHigh correlation
name is highly correlated with reviews_list and 4 other fieldsHigh correlation
online_order is highly correlated with menu_itemHigh correlation
rate is highly correlated with votes and 2 other fieldsHigh correlation
cost is highly correlated with rate and 2 other fieldsHigh correlation
cuisines is highly correlated with reviews_list and 3 other fieldsHigh correlation
rest_type is highly correlated with costHigh correlation
city is highly correlated with reviews_list and 4 other fieldsHigh correlation
book_table is highly correlated with rate and 1 other fieldsHigh correlation
menu_item is highly correlated with reviews_list and 4 other fieldsHigh correlation
address is highly correlated with reviews_list and 5 other fieldsHigh correlation
location has 744 (1.8%) zeros Zeros
rest_type has 9608 (23.3%) zeros Zeros
menu_item has 30317 (73.5%) zeros Zeros
type has 847 (2.1%) zeros Zeros
city has 727 (1.8%) zeros Zeros

Reproduction

Analysis started2021-09-25 06:10:44.470205
Analysis finished2021-09-25 06:11:15.209925
Duration30.74 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

address
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8792
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3602.695007
Minimum0
Maximum8791
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size322.3 KiB
2021-09-25T11:41:15.380723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile497
Q12071
median3435
Q35134
95-th percentile7590
Maximum8791
Range8791
Interquartile range (IQR)3063

Descriptive statistics

Standard deviation2194.930805
Coefficient of variation (CV)0.6092469111
Kurtosis-0.6904106917
Mean3602.695007
Median Absolute Deviation (MAD)1496
Skewness0.4448163439
Sum148564334
Variance4817721.237
MonotonicityNot monotonic
2021-09-25T11:41:15.621592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100286
 
0.2%
209061
 
0.1%
208049
 
0.1%
396247
 
0.1%
54643
 
0.1%
211541
 
0.1%
290639
 
0.1%
210038
 
0.1%
211837
 
0.1%
207836
 
0.1%
Other values (8782)40760
98.8%
ValueCountFrequency (%)
09
< 0.1%
14
 
< 0.1%
211
< 0.1%
32
 
< 0.1%
44
 
< 0.1%
57
< 0.1%
63
 
< 0.1%
75
< 0.1%
87
< 0.1%
97
< 0.1%
ValueCountFrequency (%)
87912
< 0.1%
87901
 
< 0.1%
87891
 
< 0.1%
87881
 
< 0.1%
87871
 
< 0.1%
87863
< 0.1%
87851
 
< 0.1%
87841
 
< 0.1%
87831
 
< 0.1%
87821
 
< 0.1%

name
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6572
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2412.058903
Minimum0
Maximum6571
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size322.3 KiB
2021-09-25T11:41:15.884429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile130
Q11001
median2140
Q33480
95-th percentile5566
Maximum6571
Range6571
Interquartile range (IQR)2479

Descriptive statistics

Standard deviation1675.999112
Coefficient of variation (CV)0.6948417012
Kurtosis-0.6322970111
Mean2412.058903
Median Absolute Deviation (MAD)1250
Skewness0.5127225667
Sum99466073
Variance2808973.023
MonotonicityNot monotonic
2021-09-25T11:41:16.032234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2186
 
0.2%
785
 
0.2%
3769
 
0.2%
5668
 
0.2%
10168
 
0.2%
13268
 
0.2%
41162
 
0.2%
11760
 
0.1%
4860
 
0.1%
16160
 
0.1%
Other values (6562)40551
98.3%
ValueCountFrequency (%)
011
 
< 0.1%
14
 
< 0.1%
211
 
< 0.1%
32
 
< 0.1%
44
 
< 0.1%
57
 
< 0.1%
63
 
< 0.1%
785
0.2%
87
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
65711
 
< 0.1%
65701
 
< 0.1%
65692
< 0.1%
65683
< 0.1%
65671
 
< 0.1%
65661
 
< 0.1%
65651
 
< 0.1%
65641
 
< 0.1%
65631
 
< 0.1%
65621
 
< 0.1%

online_order
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.3 KiB
0
27081 
1
14156 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41237
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
027081
65.7%
114156
34.3%

Length

2021-09-25T11:41:16.355037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-25T11:41:16.452029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
027081
65.7%
114156
34.3%

Most occurring characters

ValueCountFrequency (%)
027081
65.7%
114156
34.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41237
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
027081
65.7%
114156
34.3%

Most occurring scripts

ValueCountFrequency (%)
Common41237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
027081
65.7%
114156
34.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
027081
65.7%
114156
34.3%

book_table
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.3 KiB
1
34938 
0
6299 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41237
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
134938
84.7%
06299
 
15.3%

Length

2021-09-25T11:41:16.707688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-25T11:41:16.795572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
134938
84.7%
06299
 
15.3%

Most occurring characters

ValueCountFrequency (%)
134938
84.7%
06299
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41237
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
134938
84.7%
06299
 
15.3%

Most occurring scripts

ValueCountFrequency (%)
Common41237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
134938
84.7%
06299
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
134938
84.7%
06299
 
15.3%

rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.702029731
Minimum1.8
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.3 KiB
2021-09-25T11:41:16.947368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.9
Q13.4
median3.7
Q34
95-th percentile4.4
Maximum4.9
Range3.1
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.4400344597
Coefficient of variation (CV)0.118863027
Kurtosis-0.0001100394791
Mean3.702029731
Median Absolute Deviation (MAD)0.3
Skewness-0.3279338391
Sum152660.6
Variance0.1936303257
MonotonicityNot monotonic
2021-09-25T11:41:17.159086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3.93954
 
9.6%
3.83816
 
9.3%
3.73807
 
9.2%
3.63286
 
8.0%
43144
 
7.6%
4.12925
 
7.1%
3.52763
 
6.7%
3.42444
 
5.9%
3.32272
 
5.5%
4.22154
 
5.2%
Other values (21)10672
25.9%
ValueCountFrequency (%)
1.85
 
< 0.1%
211
 
< 0.1%
2.124
 
0.1%
2.226
 
0.1%
2.351
 
0.1%
2.466
 
0.2%
2.5100
 
0.2%
2.6249
0.6%
2.7303
0.7%
2.8580
1.4%
ValueCountFrequency (%)
4.955
 
0.1%
4.866
 
0.2%
4.7167
 
0.4%
4.6300
 
0.7%
4.5656
 
1.6%
4.41146
 
2.8%
4.31682
4.1%
4.22154
5.2%
4.12925
7.1%
43144
7.6%

votes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2323
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean352.7720009
Minimum0
Maximum16832
Zeros19
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size322.3 KiB
2021-09-25T11:41:17.438691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q121
median73
Q3277
95-th percentile1706
Maximum16832
Range16832
Interquartile range (IQR)256

Descriptive statistics

Standard deviation884.40923
Coefficient of variation (CV)2.507027848
Kurtosis73.40401088
Mean352.7720009
Median Absolute Deviation (MAD)64
Skewness6.868364013
Sum14547259
Variance782179.6862
MonotonicityNot monotonic
2021-09-25T11:41:17.831953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41123
 
2.7%
6979
 
2.4%
7858
 
2.1%
9735
 
1.8%
11685
 
1.7%
5659
 
1.6%
10617
 
1.5%
8617
 
1.5%
16524
 
1.3%
12463
 
1.1%
Other values (2313)33977
82.4%
ValueCountFrequency (%)
019
 
< 0.1%
12
 
< 0.1%
210
 
< 0.1%
41123
2.7%
5659
1.6%
6979
2.4%
7858
2.1%
8617
1.5%
9735
1.8%
10617
1.5%
ValueCountFrequency (%)
168323
< 0.1%
163453
< 0.1%
149562
< 0.1%
147261
 
< 0.1%
147233
< 0.1%
147172
< 0.1%
147103
< 0.1%
147041
 
< 0.1%
146941
 
< 0.1%
146901
 
< 0.1%

location
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct92
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.32807915
Minimum0
Maximum91
Zeros744
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size322.3 KiB
2021-09-25T11:41:18.011550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q114
median24
Q339
95-th percentile76
Maximum91
Range91
Interquartile range (IQR)25

Descriptive statistics

Standard deviation20.30967238
Coefficient of variation (CV)0.6924992351
Kurtosis0.1818683075
Mean29.32807915
Median Absolute Deviation (MAD)12
Skewness0.9366931089
Sum1209402
Variance412.4827922
MonotonicityNot monotonic
2021-09-25T11:41:18.168574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123873
 
9.4%
182296
 
5.6%
201993
 
4.8%
281800
 
4.4%
81710
 
4.1%
31634
 
4.0%
251568
 
3.8%
241410
 
3.4%
111226
 
3.0%
211055
 
2.6%
Other values (82)22672
55.0%
ValueCountFrequency (%)
0744
1.8%
1595
 
1.4%
217
 
< 0.1%
31634
4.0%
4158
 
0.4%
52
 
< 0.1%
662
 
0.2%
79
 
< 0.1%
81710
4.1%
989
 
0.2%
ValueCountFrequency (%)
9110
 
< 0.1%
901
 
< 0.1%
891
 
< 0.1%
888
 
< 0.1%
8723
 
0.1%
8645
 
0.1%
854
 
< 0.1%
8424
 
0.1%
833
 
< 0.1%
82506
1.2%

rest_type
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct87
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.038775857
Minimum0
Maximum86
Zeros9608
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size322.3 KiB
2021-09-25T11:41:18.416233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q39
95-th percentile34
Maximum86
Range86
Interquartile range (IQR)7

Descriptive statistics

Standard deviation12.45662471
Coefficient of variation (CV)1.549567364
Kurtosis6.704845891
Mean8.038775857
Median Absolute Deviation (MAD)2
Skewness2.451919134
Sum331495
Variance155.1674992
MonotonicityNot monotonic
2021-09-25T11:41:18.578444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
213871
33.6%
09608
23.3%
43368
 
8.2%
91850
 
4.5%
71666
 
4.0%
131278
 
3.1%
281092
 
2.6%
12704
 
1.7%
17640
 
1.6%
15639
 
1.5%
Other values (77)6521
15.8%
ValueCountFrequency (%)
09608
23.3%
1173
 
0.4%
213871
33.6%
3310
 
0.8%
43368
 
8.2%
533
 
0.1%
693
 
0.2%
71666
 
4.0%
8180
 
0.4%
91850
 
4.5%
ValueCountFrequency (%)
866
 
< 0.1%
852
 
< 0.1%
842
 
< 0.1%
8316
< 0.1%
821
 
< 0.1%
814
 
< 0.1%
804
 
< 0.1%
794
 
< 0.1%
7816
< 0.1%
775
 
< 0.1%

cuisines
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2367
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean503.270946
Minimum0
Maximum2366
Zeros89
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size322.3 KiB
2021-09-25T11:41:18.773408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q153
median253
Q3847
95-th percentile1779
Maximum2366
Range2366
Interquartile range (IQR)794

Descriptive statistics

Standard deviation576.9261429
Coefficient of variation (CV)1.146352969
Kurtosis0.6171637116
Mean503.270946
Median Absolute Deviation (MAD)220
Skewness1.257729512
Sum20753384
Variance332843.7744
MonotonicityNot monotonic
2021-09-25T11:41:19.020676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52107
 
5.1%
381949
 
4.7%
331231
 
3.0%
10620
 
1.5%
26613
 
1.5%
29600
 
1.5%
71561
 
1.4%
69545
 
1.3%
63513
 
1.2%
53409
 
1.0%
Other values (2357)32089
77.8%
ValueCountFrequency (%)
089
 
0.2%
18
 
< 0.1%
211
 
< 0.1%
3220
 
0.5%
48
 
< 0.1%
52107
5.1%
67
 
< 0.1%
785
 
0.2%
834
 
0.1%
97
 
< 0.1%
ValueCountFrequency (%)
23661
 
< 0.1%
23651
 
< 0.1%
23641
 
< 0.1%
23631
 
< 0.1%
23621
 
< 0.1%
23611
 
< 0.1%
23601
 
< 0.1%
23591
 
< 0.1%
23582
< 0.1%
23573
< 0.1%

cost
Real number (ℝ≥0)

HIGH CORRELATION

Distinct63
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean369.5862587
Minimum1
Maximum950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.3 KiB
2021-09-25T11:41:19.179921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2
Q1200
median400
Q3500
95-th percentile800
Maximum950
Range949
Interquartile range (IQR)300

Descriptive statistics

Standard deviation242.522954
Coefficient of variation (CV)0.6562012203
Kurtosis-0.7328152141
Mean369.5862587
Median Absolute Deviation (MAD)200
Skewness0.1526906702
Sum15240628.55
Variance58817.38319
MonotonicityNot monotonic
2021-09-25T11:41:19.377783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4005261
12.8%
3005242
12.7%
5004080
 
9.9%
6003189
 
7.7%
2003163
 
7.7%
2502124
 
5.2%
8002078
 
5.0%
7001817
 
4.4%
11515
 
3.7%
3501350
 
3.3%
Other values (53)11418
27.7%
ValueCountFrequency (%)
11515
3.7%
1.054
 
< 0.1%
1.1490
 
1.2%
1.2968
2.3%
1.258
 
< 0.1%
1.3511
 
1.2%
1.3518
 
< 0.1%
1.4464
 
1.1%
1.455
 
< 0.1%
1.5907
2.2%
ValueCountFrequency (%)
95060
 
0.1%
900667
 
1.6%
850149
 
0.4%
8002078
5.0%
750741
 
1.8%
7001817
4.4%
650743
 
1.8%
6003189
7.7%
550704
 
1.7%
5004080
9.9%

reviews_list
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21103
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8651.516623
Minimum0
Maximum21102
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size322.3 KiB
2021-09-25T11:41:19.621819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile313
Q13135
median7403
Q314111
95-th percentile19524.2
Maximum21102
Range21102
Interquartile range (IQR)10976

Descriptive statistics

Standard deviation6237.739506
Coefficient of variation (CV)0.72099954
Kurtosis-1.151311161
Mean8651.516623
Median Absolute Deviation (MAD)4898
Skewness0.3633891954
Sum356762591
Variance38909394.15
MonotonicityNot monotonic
2021-09-25T11:41:19.753642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
891111
 
2.7%
271421
 
0.1%
563420
 
< 0.1%
460820
 
< 0.1%
272119
 
< 0.1%
56719
 
< 0.1%
538819
 
< 0.1%
295818
 
< 0.1%
269918
 
< 0.1%
265318
 
< 0.1%
Other values (21093)39954
96.9%
ValueCountFrequency (%)
01
 
< 0.1%
13
< 0.1%
26
< 0.1%
31
 
< 0.1%
44
< 0.1%
56
< 0.1%
63
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
95
< 0.1%
ValueCountFrequency (%)
211021
< 0.1%
211011
< 0.1%
211001
< 0.1%
210991
< 0.1%
210981
< 0.1%
210971
< 0.1%
210961
< 0.1%
210951
< 0.1%
210941
< 0.1%
210931
< 0.1%

menu_item
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8243
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1064.507142
Minimum0
Maximum8242
Zeros30317
Zeros (%)73.5%
Negative0
Negative (%)0.0%
Memory size322.3 KiB
2021-09-25T11:41:19.909463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3550
95-th percentile6402.2
Maximum8242
Range8242
Interquartile range (IQR)550

Descriptive statistics

Standard deviation2125.430372
Coefficient of variation (CV)1.996633267
Kurtosis2.348151846
Mean1064.507142
Median Absolute Deviation (MAD)0
Skewness1.908787455
Sum43897081
Variance4517454.268
MonotonicityNot monotonic
2021-09-25T11:41:20.061230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030317
73.5%
180111
 
< 0.1%
16599
 
< 0.1%
17969
 
< 0.1%
42108
 
< 0.1%
45718
 
< 0.1%
6418
 
< 0.1%
45618
 
< 0.1%
19418
 
< 0.1%
8218
 
< 0.1%
Other values (8233)10843
 
26.3%
ValueCountFrequency (%)
030317
73.5%
11
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
82421
< 0.1%
82411
< 0.1%
82401
< 0.1%
82391
< 0.1%
82382
< 0.1%
82371
< 0.1%
82361
< 0.1%
82351
< 0.1%
82341
< 0.1%
82331
< 0.1%

type
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.80730897
Minimum0
Maximum6
Zeros847
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size322.3 KiB
2021-09-25T11:41:20.268954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q34
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.17050684
Coefficient of variation (CV)0.4169497736
Kurtosis-0.4717621162
Mean2.80730897
Median Absolute Deviation (MAD)1
Skewness0.2578737323
Sum115765
Variance1.370086261
MonotonicityNot monotonic
2021-09-25T11:41:20.400778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
220431
49.5%
414062
34.1%
32709
 
6.6%
11511
 
3.7%
51045
 
2.5%
0847
 
2.1%
6632
 
1.5%
ValueCountFrequency (%)
0847
 
2.1%
11511
 
3.7%
220431
49.5%
32709
 
6.6%
414062
34.1%
51045
 
2.5%
6632
 
1.5%
ValueCountFrequency (%)
6632
 
1.5%
51045
 
2.5%
414062
34.1%
32709
 
6.6%
220431
49.5%
11511
 
3.7%
0847
 
2.1%

city
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.48478308
Minimum0
Maximum29
Zeros727
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size322.3 KiB
2021-09-25T11:41:20.563764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median15
Q320
95-th percentile28
Maximum29
Range29
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.990329817
Coefficient of variation (CV)0.5516361392
Kurtosis-1.011899506
Mean14.48478308
Median Absolute Deviation (MAD)6
Skewness0.007137381607
Sum597309
Variance63.84537058
MonotonicityIncreasing
2021-09-25T11:41:20.671648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
62580
 
6.3%
192361
 
5.7%
162254
 
5.5%
172250
 
5.5%
182121
 
5.1%
121915
 
4.6%
131633
 
4.0%
111537
 
3.7%
71512
 
3.7%
231510
 
3.7%
Other values (20)21564
52.3%
ValueCountFrequency (%)
0727
 
1.8%
11208
2.9%
21072
2.6%
3956
 
2.3%
41483
3.6%
51139
2.8%
62580
6.3%
71512
3.7%
8818
 
2.0%
9953
 
2.3%
ValueCountFrequency (%)
291201
2.9%
281018
2.5%
271345
3.3%
26872
2.1%
251173
2.8%
24569
 
1.4%
231510
3.7%
221293
3.1%
21946
2.3%
201449
3.5%

Interactions

2021-09-25T11:40:51.177546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:51.397238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:51.638068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:51.885713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:52.073493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:52.269229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:52.389072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:52.529070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:52.751086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:52.938836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:53.182510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:53.322365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:53.474236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:53.681959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:53.852940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:54.069848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:54.285573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:54.449326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:54.618307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:54.798066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:54.933884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:55.089710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:55.297399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:55.437246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:55.574342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:55.709722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:55.909422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:56.065212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:56.217016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:56.348833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:56.593684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:56.745481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:56.881298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:57.009127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:57.136957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:57.316719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:57.483578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:57.615442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:57.767784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:57.919608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:58.119312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:58.259125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:58.402934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:58.543894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:58.675752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:58.830771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:59.013779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:59.169577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:59.345308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:59.490347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:59.632444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:40:59.824173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:00.022085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:00.187911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:00.367672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:00.504753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:00.676491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:00.812308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:00.984080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:01.143898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:01.267743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:01.474469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:01.630260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:01.762114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:01.961817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:02.089677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:02.213479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:02.494349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:02.626173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:02.754002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:02.881832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:03.073575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:03.205400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:03.333229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:03.466825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:03.654546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:03.789563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:03.933373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:04.088452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:04.300143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:04.443988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:04.596454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:04.712300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:04.880039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:05.055839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:05.223606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:05.431425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:05.682136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:05.873908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:05.993753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:06.113559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:06.241422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:06.409165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:06.586159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:06.777910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:06.945683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:07.073515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:07.273212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:07.413026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:07.542023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:07.685787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:07.846066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:08.033806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:08.181642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:08.309470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:08.429132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:08.554190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:08.686047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:08.821124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:08.993033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:09.124868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:09.252693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:09.388517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:09.506565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:09.746246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:09.933970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:10.057825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:10.177636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:10.305476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:10.429299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:10.562351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:10.690178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:10.881889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:11.037716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:11.177530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:11.305322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:11.445261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:11.570286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:11.690127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:11.825945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:12.069619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:12.221418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:12.360755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:12.518361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:12.665364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:12.818884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:13.006599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:13.174500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:13.302335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:13.504966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:13.712716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:13.863977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:14.055193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-25T11:41:14.290593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-09-25T11:41:20.853277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-25T11:41:21.296145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-25T11:41:21.750129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-25T11:41:22.013304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-25T11:41:22.318267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-25T11:41:14.604713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-25T11:41:14.997150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

addressnameonline_orderbook_tableratevoteslocationrest_typecuisinescostreviews_listmenu_itemtypecity
000004.1775000800.00000
111014.1787001800.01000
222013.8918012800.02000
333113.788023300.03000
444113.8166104600.04000
555013.8286105600.05000
666113.68206800.06000
777004.62556037600.07010
888014.0324048700.08010
999014.2504049550.09010

Last rows

addressnameonline_orderbook_tableratevoteslocationrest_typecuisinescostreviews_listmenu_itemtypecity
4122787022372104.0189254210601.5209190629
4122833152833003.8128253312111.242150629
4122987356527113.72725112041.2209390629
4123028592509113.97725682372.037270629
4123128702512112.8161252811021.237300629
4123231372699113.7342528204800.040280629
4123387911716112.5812528761800.0210820629
4123487256532113.62725172401.5209560629
4123587866568104.323656172372.5210540629
4123634446569113.413563318701.5210550629